• Baby hipsters going to baby graduate school
• Small (1400 students) undergraduate only liberal arts college
  • Smaller class sizes
  • Very motivated and quirky student body
  • Socially progressive but academically conservative
• Established in 1908 because founder
  • Didn't care for sports
  • Despised social clubs
• Huge de-emphasis on grades
• High degree of interaction between departments

• Not a vocational school
• Among the schools with the highest proportion of students who do a PhD
• All students must:
  • take a junior qualifying exam
  • write a senior thesis
  • take freshman humanities class: HUM 110

• Statistics is within the math department.
• Classes:
  • Introductory statistics
  • Year-long junior level probability & mathematical statistics sequence
  • A few methods classes in other departments
• Until this semester, no applied stats class taught by statistician

• MATH241: Case Studies in Statistical Analysis
• Should have called it "Introduction to Data Science"

Concern from the statistics community

• Former ASA president Marie Davidson "Aren't we data science?"
• Former IMS president Bin Yu "Let us own data science"
• UC Davis Prof. Norman Matloff "Statistics Losing Ground To CS, Losing Image Among Students"

Former Googler Rachel Schutt taught a similar Data Science class course at Columbia University.

Prereqs

Only intro stats (know what a standard error and regression are) and some exposure to R

Syllabus

• 6-7 biweekly mini-reports
  • varying degree of open-endedness
  • submitted in R Markdown to encourage reproducible research
  • Large amount of feedback from me
• Term project: both report and 20 min oral presentation
• In-class participation

18 students, mostly juniors and seniors.

ASA's GAISE Reports

• Use real data.
• Stress conceptual understanding, rather than mere knowledge of procedures.
• Foster active learning in the classroom.
• Use technology for developing conceptual understanding and analyzing data.

• Messy data, from potentially disparate sources
• Bottom-up: Let questions/data motivate the statistical methodology, rather than vice-versa
• Discussions in class
• Lean on R heavily
• Focus on the entire analysis pipeline: article in Nature

• Key: Forget Base R
• How? The Hadleyverse.
• In particular
  • dplyr package for data wrangling
  • ggplot2 package for data visualization

We set the restriction that all our data exists in a matrix called a data frame, which we say has the "tidy" property:

Most data manipulations can be achieved by the following verbs on a "tidy" data:

1. filter: keep rows matching criteria
2. summarise: reduce variables to values
3. mutate: add new variables
4. arrange: reorder rows
5. select: pick columns by name
6. join: join two data frames
7. group_by: group subsets of observations together

The pipe %>% command, pronounced "then".

For example: say you want to apply functions h() and g() and then f() on data x. You can do

• f(g(h(x))) OR
• h(x) %>% g() %>% f()

A statistical graphic consists of a mapping of variables in data to aesthetic attributes of geometric objects that we can observe.

ggplot2 allows us to construct graphics in a modular fashion by specifying these components.

Dataset consisting of all 227,496 flights domestic flights leaving Houston airport (IAH) in 2011. Four data frames:

• flights: flight info
• weather: hourly weather info
• planes: information on all 2853 airplanes
• airports: destination airport information

Rennie Meyers

Will Jones

Sample of 10% of San Francisco OkCupid users in June 2012 (\(n=5995\)). 40.2% of the population was female.

Goal was to use logistic regression to predict gender.

Miguel Connor

Many students

Shiny App

• Houston Flights data
• OkCupid data
• Reed jukebox data: Talking Heads
• Elections data
• Quandl
• Babynames datasets
• Census data (I love FIPS codes!) joined with health data joined with shapefile data

• Regressions, time series, spatial autocorrelation (show image), density plots (prostitution in Portland), text mining, sampling bias, First law of geography
• Webscraping using the rvest package
• GitHub and R Markdown for reproducibility
• Good programming practice (Google style guide)
• GitHub for promoting/sharing work
• Dates with lubridate
• String manipulation and basic regular expressions with stringr

• Importance of exploratory data analysis
  • Time zones of Reed Jukebox
  • Erroneous join of counties in US
• Miguel's quandl analysis

• One econ student who used to be STATA, is now building a Shiny app for his senior thesis.
• One biology major, most useful class he's taken at Reed.
• One biology major said if she took this class earlier in her career, it would have convinced her to be a Math major.

• Statistical topics: More on dependent data, missing data, causal inference, and some machine learning
• Databases/SQL
• Ask better questions of the students
• Flipped-classroom
  • Lab exercises at home
  • Problem solving/debugging and discussion in class

• Typical conversation
  • Me (statistician): "Hi, my name is Albert. I'm a statistician."
  • Other party: "Statistics? I hated that class."
• Atypical conversation
  • Me (statistician): "Hi, my name is Albert. My work involves a lot of data visualization."
  • Other party: "Data visualization? I hate that stuff."

Data visualization is a backdoor way to get students interested in statistics.

Trick them into thinking they're not learning and they do.

This is the only stats class many will take.

• Get them looking, manipulating, and visualizing data quick
• Better integration of lectures and labs
• Intro stats:
  • Boost data visualization
  • Randomization based methods. Tim Hesterberg's paper What Teachers Should Know about the Bootstrap: Resampling in the Undergraduate Statistics Curriculum

• Point-and-click vs command line.
  
• Thinking algorithmically
• Debugging: help files and Google
• Like learning a language

• A statistics class focused on the data
• Rich Majerus wrote "Why should students at a small liberal arts college learn R?"
  • He learned R using dplyr and ggplot, not base R.
  • New tools like Datacamp are increasing: \[\frac{\mbox{Payoff from learning R}}{\mbox{Startup costs}}\]
• Data visualization as a "backdoor entrance" for statistics
• Developing skills and intuition takes time. At Reed classes are small: attention and feedback
• Interactivity boosts student interest

Presentation on 2011/06/27 given by Dierdre and Amir:

• Look at your data ASAP.
• Don't thrash
  • Do your due diligence, but don't overdo.
  • Seek expert advice!
    
• "You actually don’t know what you are doing until after you have done it!"
• Do the most braindead thing first, take it end to end, then iterate and improve.
• Fight perfectionistism: think of the marginal return of your efforts.

Ressources

In the repositories section of github.com/rudeboybert

• Slides for this talk: Teaching Data Science Ugrads
• All the materials for the class: MATH241